3 citations · 9 across the 8 of their papers we have counts for
4 papers · 1 filter
Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction
Mathieu Blondel, Michael E. Sander, Germain Vivier-Ardisson +2
Autoregressive models (ARMs) currently constitute the dominant paradigm for large language models (LLMs). Energy-based models (EBMs) represent another class of models, which have h…
Loss Functions and Operators Generated by f-Divergences
Vincent Roulet, Tianlin Liu, Nino Vieillard +2
The logistic loss (a.k.a. cross-entropy loss) is one of the most popular loss functions used for multiclass classification. It is also the loss function of choice for next-token pr…
Joint Learning of Energy-based Models and their Partition Function
Michael E. Sander, Vincent Roulet, Tianlin Liu +1
Energy-based models (EBMs) offer a flexible framework for parameterizing probability distributions using neural networks. However, learning EBMs by exact maximum likelihood estimat…
Decoding-time Realignment of Language Models
Tianlin Liu, Shangmin Guo, Leonardo Bianco +7
Aligning language models with human preferences is crucial for reducing errors and biases in these models. Alignment techniques, such as reinforcement learning from human feedback…